Dental product material identification method based on combination of machine vision and near infrared spectroscopy

Abstract

The invention provides a dental product material identification method based on combination of machine vision and near infrared spectroscopy. The method is characterized by comprising the following steps: S1, collecting images of three to-be-identified sample dental models through a camera, and preprocessing the images; S2, performing near-infrared information principal component feature extraction on the three sample dental model images, and identifying the materials of the three main dental products; and S3, fusing the principal component score characteristics extracted from the near infrared spectrum of the dental model based on a Softmax classifier, and identifying different ceramic dental products. According to the method, the accuracy and the rapidity of dental product material identification can be effectively improved.

Description

technical field [0001] The invention relates to the technical field of material identification and the field of computer technology, in particular to a material identification method for dental products based on the combination of machine vision and near-infrared spectroscopy. Background technique [0002] With the continuous improvement of living standards, many people have various problems with their teeth due to bad eating habits. In severe cases, the teeth need to be pulled out, so that dentures are needed for repairs so as not to affect chewing ability. and beautiful. [0003] Dentures are generally divided into plastic teeth, metal teeth and ceramic teeth according to the material. In the production process of dental restoration products, quality inspection of finished products is required. Due to the limited ability of people to identify materials, it is impossible to quickly and effectively judge the materials of dental products. Therefore, for large-scale dental p...

AI Technical Summary

Problems solved by technology

Among them, image recognition technology has poor adaptive performance. Once the target image is polluted by strong noise or the target image has a large defect, ideal results are often not obtained; radio frequency technology has high inspection costs due to radiation to the human body. Crack type defects have directional restrictions; ultrasonic technology and infrared technology have high costs, small production batches, and rapid replacement

Therefore, the above-mentioned detection methods are not well applicable to the quality inspection process in dental production, so it is necessary to develop a fast and effective material identification method for dental products

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